In comparison to other work domains and to the current state of research, we face on the manufacturing shop floor little attention for the role of informal learning as key driver for manual assembly processes. There, smart assistance systems focus on process qualities and outcomes, not on the workers' abilities to cope with novel or uncertain work situations. In this paper we present a conceptual approach on how to integrate cognitive automation and self-directed learning at the assembly workplace. We argue for embedding smart learning behavior, based on the model of cognitive apprenticeship and provided by a cognitive architecture, with existing cognitive automation approaches. The conceptual approach is illustrated with the Plant@Hand smart assembly assistant prototype for the industrial assembly workplace.
In comparison to other work domains and to the current state of research, we face on the manufacturing shop floor little attention for the role of informal learning as key driver for manual assembly processes. There, smart assistance systems focus on process qualities and outcomes, not on the workers' abilities to cope with novel or uncertain work situations. In this paper we present a conceptual approach on how to integrate cognitive automation and self-directed learning at the assembly workplace. We argue for embedding smart learning behavior, based on the model of cognitive apprenticeship and provided by a cognitive architecture, with existing cognitive automation approaches. The conceptual approach is illustrated with the Plant@Hand smart assembly assistant prototype for the industrial assembly workplace.
Whereas the former Web mostly consisted of information represented in textual documents, nowadays the Web includes a huge number of multimedia documents like videos, photos, and audio. This enormous increase in volume in the private, and above all in the industry sector, makes it more and more difficult to find relevant information. Besides the pure management of multimedia documents, finding hidden semantics and interconnections of heterogeneous cross-media content is a crucial task and stays mostly untouched. To overcome this tendency we see the need for a generic cross-media analysis platform, ranging from extracting relevant features from media objects over representing and publishing extraction results to integrated querying of aggregated findings. In this paper we propose the underlying foundation for a common and contextual multimedia platform in terms of an unified model for publishing multimedia analysis results. The proposed model is based on existing ontologies, adapted and extended to the cross-media environment. Besides the introduction of the already mentioned platform and model, this paper also briefly introduces specific use-case applications as well as possibilities to query the persisted data.
In this paper, we report preliminary results of a small-scale case study about the data citation quantity and quality in research output of the National Educational Panel Study (NEPS), a longitudinal study analyzing educational processes in Germany across the lifespan. In order to collect research output based on NEPS data, we searched for and examined publications of a randomly selected sample of 72 NEPS data users. Altogether, we found 18 publications to be relevant for citation analysis. Compared to previous studies, the citation behavior in our sample can be assessed as better. However, publications often lack the inclusion of central data citation elements, such as a persistent identifier. The quality of data citations seems to vary across different types of research output. In a follow-up study, we plan to do a comprehensive sampling and analysis of NEPS related research output in order to verify our findings, and also to include further panel studies to compare citation behavior across different studies.
The increasing number of co-authored academic papers points to the importance of collaborative writing in contemporary research. Digital technologies add a new dimension to collaborative writing by providing co-authors with access to the same document and enabling co-authors to edit the shared text at the same time. The availability of web-based tools for collaborative writing prompts the question of the extent to which researchers incorporate these tools into their scholarly practices. Based on my statistical analysis of the data from the Science 2.0 Survey (2014), conducted in cooperation with the Leibniz Research Alliance Science 2.0, I examine the usage of digital technologies in the process of collaborative writing among researchers in Germany. I use the concepts of asynchronous and synchronous modes of writing, derived from the field of Computer Supported Cooperative Work, to discuss collaborative writing strategies in the context of Science 2.0. My study shows that researchers use a mixture of different writing strategies and that they tend to use the same tool for different writing strategies. Moreover, I discuss researchers' attitudes towards online text editors. In reflecting on collaborative writing, I consider both the technological and social aspects.
While Citizen Science projects involve people in passive or active project tasks, Citizen Inquiry offers the opportunity for deeper involvement through initiating and facilitating science investigations. This study aims to explore the creation and evolution of Weather-it, a Citizen Inquiry online community hosted by the nQuire-it platform. Weather-it enables people to create and maintain their own weather missions (investigations), to which other people can contribute. The evolution of Weather-it community is explored through social network graphs of Weather-it members and their interactions. Information regarding other aspects of the community such as the type of members, their recruitment and motivations, and the identity and sustainability of the community, is collected through a survey comprising open and closed-ended questions. The results indicate differences in these community engagement aspects between Citizen Science and Citizen Inquiry projects, providing insight into the behaviour of people in projects that require more active involvement throughout the scientific investigations.
This paper presents a case study on co-designing digital technologies for knowledge management and data-driven business for an SME. The goal of the case study was to analyse the status quo of technology usage and to develop design suggestions in form of mock-ups tailored to the company's needs. We used both requirements engineering and interactive system design methods such as interviews, workshops, and mock-ups for work analysis and system design. The case study illustrates step-by-step the processes of knowledge extraction and combination (analysis) and innovation creation (design). These processes resulted in non-functional mock-ups, which are planned to be implemented within the SME.
Knowledge visualization is an effective instrument for knowledge creation, acquisition and transfer. Knowledge maps are the most common knowledge visualization techniques, specifically, mind maps. Managers frequently use these instruments in their work for business analytics purposes, but the preparation of such maps must follow several laws in order to make them effective. The goal of the current paper is to develop the methodology and some practical recommendations how to design knowledge maps that can be used for knowledge codification, transfer, sharing and dissemination in companies. The paper also evaluates proposed visualization laws for creating knowledge maps based on the principles of cognitive psychology. The knowledge maps visual design methodology is based on perceptual factors and include the law of pragnanz (the law of good shape) and the law of parsimony (the Ockham's razor principle). The results were obtained through the qualitative analysis of group mind maps of 48 top-managers of Russian companies. We may assert that for the knowledge map to be effective in knowledge codification, transfer, sharing and dissemination in companies it should follow laws of good shape, parsimony and tips related to knowledge maps design. The proposed framework of visualization laws is important for many reasons. It is targeted at the development of methodologies and related technologies that can scaffold the process of knowledge structuring and transfer for managers' business analytics tasks and decision-making. The paper contributes to managerial practice by describing the practical recommendations for effective visual knowledge structuring.
Process Oriented Training and Learning can be applied in two different approaches: (a) processes describing the methodology of training and learning as well as (b) processes describing the organizational context that need to be learned. This paper intrudes the results of the EU project Learn PAd that developed prototypes of modelling tools enabling business processes for learning and training. Flexibility of business processes have been introduced with case management and knowledge artefacts of PROMOTE had been integrated to provide a complete modelling environment fulfilling the identified 101 requirements for the modelling language. The local deployment and the Web-based deployment of the developed prototypes are introduced and the development space that enables collaborative participation of the development and improvement of the prototypes on ADOxx.org is introduced.
This paper introduces our method of the UML diagrams visualization in 3D space. It uses the layers for particular components and modules in class diagram, alternative and parallel scenarios in sequence and activity diagrams with modern combined frameworks. The herein presented approach contains also automatic generating of the object diagrams and final class diagram from the sequence diagrams of the use case scenarios. It applies force directed algorithm to create more convenient automated class diagram layout using semantics by adding weight factor in force calculation process.
The random surfer model is a frequently used model for simulating user navigation behavior on the Web. Various algorithms, such as PageRank, are based on the assumption that the model represents a good approximation of users browsing a website. However, the way users browse the Web has been drastically altered over the last decade due to the rise of search engines. Hence, new adaptations for the established random surfer model might be required, which better capture and simulate this change in navigation behavior. In this article we compare the classical uniform random surfer to empirical navigation and page access data in a Web Encyclopedia. Our high level contributions are (i) a comparison of stationary distributions of different types of the random surfer to quantify the similarities and differences between those models as well as (ii) new insights into the impact of search engines on traditional user navigation. Our results suggest that the behavior of the random surfer is almost similar to those of users---as long as users do not use search engines. We also find that classical website navigation structures, such as navigation hierarchies or breadcrumbs, only exercise limited influence on user navigation anymore. Rather, a new kind of navigational tools (e.g., recommendation systems) might be needed to better reflect the changes in browsing behavior of existing users.
In the last two decades, research on knowledge management (KM) has shifted its focus to understanding the role of knowledge management tools in achieving business objectives. However, KM initiatives often remain dispersed, especially in highly distributed organizational settings, such as business networks. Existing research on cross-organizational knowledge management focuses primarily on efficiencies through shared services while neglecting a knowledge sharing and creation perspective. Here we take a knowledge maturing perspective to propose a model for requirements knowledge which - instead of looking at single activities of retrieval -- considers a continuous cycle where the knowledge intensive processes and requirements shape each other. We develop the model using the insight from a case study of a European business network. Our findings for meeting innovation requirements reflect that the selective access to communication platforms due to the less formal network structure needs to be substituted by process based roles and tasks of employees. Considering efficiency requirements diverse data sources result in the need to capture and incorporate the semantics of concepts for elimination of duplicated process related effort. Regarding quality requirements the guidance role of matured knowledge, such as standards, best practices, controls etc. needs to be integrated.
Usually, knowledge workers are said to not benefit from business process management (BPM) systems, since their main tasks are weakly structured and not representable by a workflow. However, not all of their tasks are equally weak structured, and with adaptive case management (ACM) solutions, a new category of tools came up to support those processes, even if they are weakly structured. This paper introduces an approach to support the creation of cases for ACM engines by mining activities from an activity stream and suggesting tasks that a knowledge worker can use to create a case. Furthermore, the approach supports maturing of the case towards a workflow by detecting repeating sequences in the execution of tasks and suggesting sub processes for the case which is possible with the case management model and notation (CMMN) together with the business process model and notation (BPMN). To allow for further enhancement of the cases, the ACM solution is extended with social collaboration features, so that people working on the case can comment and rate single tasks. The goal is to show that it is possible to establish ties from social activities and Web 2.0 to ACM and BPM. The presented solution uses a graph database as a basis for activity mining.
Data about how individuals explore an information space and collect facts about their topic of interest is valuable to analyze but difficult to come by. In this paper we outline how this data can be captured in a search task with the help of a special search environment. Domain experts sharing this data in an organization can enhance their collaborative search experience and benefit from each others' search and domain expertise. Our approach facilitates interaction mechanisms of an interface and data mining methods. We also lay out a business process where the system is applied.
In recent years, investigating opportunities to support knowledge-intensive business processes has gained increasing momentum in the research community. Novel contributions that introduce paradigms addressing the need for process execution flexibility form an alternative to traditional workflow management approaches and are mostly subsumed under the concept of adaptive case management (ACM). However, many of these approaches omit mining any kind of knowledge about such processes. This is because there is a gap between process mining, which works well for structured processes, and ACM, which mainly focuses on information system support for task management and collaboration using heterogeneous data sources. In this paper, we strive to bridge this gap by introducing a method for mining knowledge-intensive processes. It is part of agenda-driven case management, an ACM approach that follows the idea of mining common execution patterns while a case manager handles a flexible agenda.
Advanced manufacturing promises an evolution of industrial production processes. However, today's manufacturing systems lack a common strategy on how to combine factual, procedural, and conceptual knowledge in order to streamline production processes. This specifically applies for manufacturing assembly assistance where the major share of procedural and conceptual knowledge is not yet automatically processable. In our paper we propose the usage of ontology-based annotations as missing link between the tacit knowledge of the worker and the intelligent assistance system. We show the deeper integration of conceptual knowledge modeled in ontology-based annotations with procedural knowledge in cognitive architectures. Additionally, in our approach annotations act as a mean of communication between the workers and with the system. We show key aspects of a prototypical integration of our approach within a smart assembly assistance system which supplies the worker with task related information.
The use of social media amongst children, adolescents and families is nowadays a common practise in our everyday lives. Social networking sites allow social interaction between people through various channels, such as Twitter, Facebook, YouTube and blogs. Even if this interaction is generally healthy, these sites bring several risks, such as cyberbullying, depression and exposure of inappropriate content. In this paper we tackle the problem of cyberbullying via a novel approach that analyses online posts in trending world events. These generally cause a lot of interest and controversy among online Web users. Twitter is the social network of choice, where a large dataset of tweets is collected. The two current world events selected are the Ebola virus outbreak in Africa and the shooting of Michael Brown in Ferguson, Missouri. Collected tweets are carefully analysed to identify the most popular hashtags and named entities used within cyberbullying tweets. This analysis provides a basis towards several useful applications, such as a cyberbullying online post detector for certain current trending world events. This will help reduce the number of cyberbullying cases in social networking sites. Results obtained from this evaluation can be applied to other cyberbullying scenarios.
Word clouds are used for the visual representation of texts. The font size and color of a word show its importance, and the position of a word in the cloud can be arbitrary or reflect its relation to other words. In this paper, we present a tool that generates concept clouds from German company websites. The main idea of the visualization is to show the overall work and main interests of companies in a detailed information cloud based solely on their own web page. The concepts are taken from the STW Thesaurus of Economics. The colors of the concepts show the categories of the concepts in the thesaurus while the cloud layout is organized by semantic proximity of the concepts. To compute the similarity between concepts we use the semantic representation that is generated from DeWaC corpus. The distributional similarity is fundamentally different from the co-occurrence statistics which often used to generate word clouds.
Knowledge-intensive processes are difficult to support because of their complexity, high variability and unpredictable information requirements. Therefore such process types are handled manually by knowledge workers with expertise in the domain. Yet to make informed decisions, knowledge workers require a multitude of domain specific, case-related information. This often leads to a time-consuming search for information and knowledge required to address the issues occurring in the case. To reduce the time spent searching for information, we propose an ontology-based recommender system that provides case-related information based on documents gathered in accumulated similar cases. The recommender system builds models of domain specific concepts for past cases as well as for the current case, which are used for case similarity calculation. To evaluate the performance of parts of our approach we used the OHSUMED document collection and compared the cosine similarity measure of ontological case model against textual case model.
